Negative Binomial Mixed Models for Analyzing Longitudinal Microbiome Data.

ABSTRACT: The metagenomics sequencing data provide valuable resources for investigating the associations between the microbiome and host environmental/clinical factors and the dynamic changes of microbial abundance over time. The distinct properties of microbiome measurements include varied total sequence reads across samples, over-dispersion and zero-inflation. Additionally, microbiome studies usually collect samples longitudinally, which introduces time-dependent and correlation structures among the samples and thus further complicates the analysis and interpretation of microbiome count data. In this article, we propose negative binomial mixed models (NBMMs) for longitudinal microbiome studies. The proposed NBMMs can efficiently handle over-dispersion and varying total reads, and can account for t

SUBMITTER: Zhang X 

PROVIDER: S-EPMC6070621 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature